"""simple docstring"""



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=3    ,		lowerCamelCase=32    ,		lowerCamelCase=3    ,		lowerCamelCase=10    ,		lowerCamelCase=[10, 20, 30, 40]    ,		lowerCamelCase=[1, 1, 2, 1]    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase="relu"    ,		lowerCamelCase=3    ,		lowerCamelCase=None    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    image_size
        __a       =    num_channels
        __a       =    embeddings_size
        __a       =    hidden_sizes
        __a       =    depths
        __a       =    is_training
        __a       =    use_labels
        __a       =    hidden_act
        __a       =    num_labels
        __a       =    scope
        __a       =    len(lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.num_labels						)

        __a       =    self.get_config()
        return config, pixel_values, labels


 def 						a__   ( self						):
        return RegNetConfig(
            num_channels=self.num_channels    ,		embeddings_size=self.embeddings_size    ,		hidden_sizes=self.hidden_sizes    ,		depths=self.depths    ,		hidden_act=self.hidden_act    ,		num_labels=self.num_labels    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFRegNetModel(config=lowerCamelCase						)
        __a       =    model(lowerCamelCase    ,		training=lowerCamelCase						)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    TFRegNetForImageClassification(lowerCamelCase						)
        __a       =    model(lowerCamelCase    ,		labels=lowerCamelCase    ,		training=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        __a ,   __a ,   __a       =    config_and_inputs
        __a       =    {"pixel_values": pixel_values}
        return config, inputs_dict






@require_tf
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[str]							  =						(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
 _snake_case       :					List[str]							  =						(
     {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
     if is_tf_available()
     else {}
 )

 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					Any							  =						False
 _snake_case       :					Optional[int]							  =						False
 _snake_case       :					Optional[int]							  =						False
 _snake_case       :					int							  =						False


 def 						a__   ( self						):
        __a       =    TFRegNetModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase    ,		has_text_modality=lowerCamelCase						)


 def 						a__   ( self						):
        return


 @unittest.skip(reason="RegNet does not use inputs_embeds"						)
 def 						a__   ( self						):
        pass


 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0    ,		reason="TF does not support backprop for grouped convolutions on CPU."    ,		)
 @slow
 def 						a__   ( self						):
        super().test_keras_fit()


 @unittest.skip(reason="RegNet does not support input and output embeddings"						)
 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a       =    model_class(lowerCamelCase						)
               __a       =    inspect.signature(model.call						)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a       =    [*signature.parameters.keys()]

               __a       =    ["pixel_values"]
               self.assertListEqual(arg_names[:1]    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase						)


 def 						a__   ( self						):
        def check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               __a       =    model_class(lowerCamelCase						)
               __a       =    model(**self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)    ,		training=lowerCamelCase						)

               __a       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __a       =    self.model_tester.num_stages
               self.assertEqual(len(lowerCamelCase						)    ,		expected_num_stages + 1						)

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]						)    ,		[self.model_tester.image_size // 2, self.model_tester.image_size // 2]    ,		)

        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()
        __a       =    ["basic", "bottleneck"]
        for model_class in self.all_model_classes:
               for layer_type in layers_type:
                      __a       =    layer_type
                      __a       =    True
                      check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      __a       =    True

                      check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase={}						):
               __a       =    model(lowerCamelCase    ,		return_dict=lowerCamelCase    ,		**lowerCamelCase						)
               __a       =    model(lowerCamelCase    ,		return_dict=lowerCamelCase    ,		**lowerCamelCase						).to_tuple()

               def recursive_check(lowerCamelCase    ,		lowerCamelCase						):
                      if isinstance(lowerCamelCase    ,		(List, Tuple)						):
                             for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase    ,		lowerCamelCase						):
                                    recursive_check(lowerCamelCase    ,		lowerCamelCase						)
                      elif tuple_object is None:
                             return
                      else:
                             self.assertTrue(
                                 all(tf.equal(lowerCamelCase    ,		lowerCamelCase						)						)    ,		msg=(
                                     "Tuple and dict output are not equal. Difference:"
                                     F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"
                                 )    ,		)

                      recursive_check(lowerCamelCase    ,		lowerCamelCase						)

        for model_class in self.all_model_classes:
               __a       =    model_class(lowerCamelCase						)

               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
               check_equivalence(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase    ,		return_labels=lowerCamelCase						)
               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase    ,		return_labels=lowerCamelCase						)
               check_equivalence(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
               check_equivalence(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		{"output_hidden_states": True}						)

               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase    ,		return_labels=lowerCamelCase						)
               __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase    ,		return_labels=lowerCamelCase						)
               check_equivalence(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		{"output_hidden_states": True}						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)






 @slow
 def 						a__   ( self						):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =    TFRegNetModel.from_pretrained(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)



def 	_lowerCamelCase(	):
       __a       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
       return image






@require_tf
@require_vision
class  snake_case__      (       unittest.TestCase       ):


 @cached_property
 def 						a__   ( self						):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
            if is_vision_available()
            else None
        )






 @slow
 def 						a__   ( self						):
        __a       =    TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

        __a       =    self.default_image_processor
        __a       =    prepare_img()
        __a       =    image_processor(images=lowerCamelCase    ,		return_tensors="tf"						)

        # forward pass
        __a       =    model(**lowerCamelCase    ,		training=lowerCamelCase						)

        # verify the logits
        __a       =    tf.TensorShape((1, 1000)						)
        self.assertEqual(outputs.logits.shape    ,		lowerCamelCase						)

        __a       =    tf.constant([-0.4180, -1.5051, -3.4836]						)

        tf.debugging.assert_near(outputs.logits[0, :3]    ,		lowerCamelCase    ,		atol=1E-4						)

"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
